Agent Post-Training Research

About the Position

Agent Post-Training Research

San Francisco

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About the Team

The Agent Post-Training team at OpenAI is at the forefront of developing the frontier agents that power our products like Codex, ChatGPT, and the API. We are dedicated to creating persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand the possibilities of what people and organizations can achieve.

Our work involves defining the capabilities of the next generation of agents, building the training signals that teach these abilities, and conducting experiments to bring them to life. This spans coding, tool use, computer operation, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and more.

Our team is where new model capabilities are born. We create the data, environments, graders, training methods, and feedback loops that shape what our next agents can do. We then carry these capabilities through major training runs and into the products used by people around the world.

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About the Role

As a member of the Agent Post-Training team, you will enhance the capabilities, reliability, and product fit of OpenAI's agentic models. You might own a research direction, build infrastructure to make large training runs faster and more trustworthy, create evaluations that reveal where models fail, or drive a capability from an idea through experimentation, integration, and launch.

This role is intentionally broad, seeking individuals who can make progress across research, engineering, data, evaluations, and product. You should be excited to work on models that act in the world: writing and debugging code, using tools, calling functions, operating computers, collaborating with other agents, and completing valuable work on behalf of users.

You will collaborate with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for those who want their work to directly impact frontier models. This role may be based in San Francisco or London, subject to team needs and location approval.

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Responsibilities

• Design and run experiments that improve agentic model behavior across coding, tool use, function calling, computer use, multi-agent collaboration, long-horizon tasks, factuality, instruction following, and calibrated reasoning.
• Own end-to-end improvements to the post-training stack, including reinforcement learning, data pipelines, graders, reward signals, evaluations, diagnostics, and model-behavior analysis.
• Build evaluations and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions.
• Partner with Codex, API/platform, and ChatGPT product teams to understand user needs and translate product signals into model improvements.
• Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and evaluation loops that shape downstream agent behavior.
• Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs.
• Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.
• Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments.
• Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes.

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Requirements

• Strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and the ability to learn quickly across areas you have not worked in before.
• Hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evaluations, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems.
• Excitement for open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution.
• A focus on product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with.
• Ability to move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next.
• Comfort working across research, product, infrastructure, data, evaluations, and safety boundaries, and the ability to communicate clearly with each group.
• A willingness to build load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.
• A desire to train and ship models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.

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About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.

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